InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery
arXiv:2606.16133v1 Announce Type: cross Abstract: Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed-loop discovery depends not only on prediction performance, but also on whether expensive first-principles results are independently validated, provenance-recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. He
The increasing sophistication of AI and computational materials science is enabling more robust, closed-loop materials discovery frameworks, specifically targeting complex properties like carrier mobility, which requires detailed validation.
This development can significantly accelerate the discovery of advanced materials for critical technologies, by integrating AI with first-principles methods in a reliable, feedback-driven system.
The reliability-gated feedback loop ensures that AI-driven inverse materials design is not just fast but also robust and trustworthy, critical for practical applications and reducing experimental validation costs.
- · Materials science researchers
- · Semiconductor industry
- · AI-driven drug discovery
- · Traditional, trial-and-error materials discovery methods
Faster and more efficient discovery of novel materials with optimized properties.
Reduced R&D costs and accelerated time-to-market for products relying on advanced materials.
Potential for new technological breakthroughs across various sectors previously limited by material capabilities.
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Read at arXiv cs.AI